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Title: Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale

Abstract

A strict throughput requirement has placed a cap on the degree to which we can depend on the execution of single, long, fine spatial grid simulations to explore global atmospheric climate behavior. Alternatively, running an ensemble of short simulations is computationally more efficient. We test the null hypothesis that the climate statistics of a full-complexity atmospheric model derived from an ensemble of independent short simulation is equivalent to that from an equilibrated long simulation. The climate of short simulation ensembles is statistically distinguishable from that of a long simulation in terms of the distribution of global annual means, largely due to the presence of low-frequency atmospheric intrinsic variability in the long simulation. We also find that model climate statistics of the simulation ensemble are sensitive to the choice of compiler optimizations. While some answer-changing optimization choices do not effect the climate state in terms of mean, variability and extremes, aggressive optimizations can result in significantly different climate states.

Authors:
 [1];  [1];  [1];  [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Earth Sciences and Climate Change Science Inst.
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility and Climate Change Science Inst.
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1567444
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Procedia Computer Science
Additional Journal Information:
Journal Volume: 108; Journal Issue: C; Journal ID: ISSN 1877-0509
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Computer Science; reproducibility; climate simulation; ensemble testing

Citation Formats

Mahajan, Salil, Gaddis, Abigail L., Evans, Katherine J., and Norman, Matthew R. Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale. United States: N. p., 2017. Web. https://doi.org/10.1016/j.procs.2017.05.259.
Mahajan, Salil, Gaddis, Abigail L., Evans, Katherine J., & Norman, Matthew R. Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale. United States. https://doi.org/10.1016/j.procs.2017.05.259
Mahajan, Salil, Gaddis, Abigail L., Evans, Katherine J., and Norman, Matthew R. Fri . "Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale". United States. https://doi.org/10.1016/j.procs.2017.05.259. https://www.osti.gov/servlets/purl/1567444.
@article{osti_1567444,
title = {Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale},
author = {Mahajan, Salil and Gaddis, Abigail L. and Evans, Katherine J. and Norman, Matthew R.},
abstractNote = {A strict throughput requirement has placed a cap on the degree to which we can depend on the execution of single, long, fine spatial grid simulations to explore global atmospheric climate behavior. Alternatively, running an ensemble of short simulations is computationally more efficient. We test the null hypothesis that the climate statistics of a full-complexity atmospheric model derived from an ensemble of independent short simulation is equivalent to that from an equilibrated long simulation. The climate of short simulation ensembles is statistically distinguishable from that of a long simulation in terms of the distribution of global annual means, largely due to the presence of low-frequency atmospheric intrinsic variability in the long simulation. We also find that model climate statistics of the simulation ensemble are sensitive to the choice of compiler optimizations. While some answer-changing optimization choices do not effect the climate state in terms of mean, variability and extremes, aggressive optimizations can result in significantly different climate states.},
doi = {10.1016/j.procs.2017.05.259},
journal = {Procedia Computer Science},
number = C,
volume = 108,
place = {United States},
year = {2017},
month = {6}
}

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Works referencing / citing this record:

Machine Learning Predictions of a Multiresolution Climate Model Ensemble
journal, May 2018

  • Anderson, Gemma J.; Lucas, Donald D.
  • Geophysical Research Letters, Vol. 45, Issue 9
  • DOI: 10.1029/2018gl077049

A Multivariate Approach to Ensure Statistical Reproducibility of Climate Model Simulations
conference, June 2019

  • Mahajan, Salil; Evans, Katherine J.; Kennedy, Joseph H.
  • PASC '19: Platform for Advanced Scientific Computing Conference, Proceedings of the Platform for Advanced Scientific Computing Conference
  • DOI: 10.1145/3324989.3325724

LIVVkit 2.1: automated and extensible ice sheet model validation
journal, January 2019

  • Evans, Katherine J.; Kennedy, Joseph H.; Lu, Dan
  • Geoscientific Model Development, Vol. 12, Issue 3
  • DOI: 10.5194/gmd-12-1067-2019